Laboratory

Population Dynamics and Sustainable Well-Being

At a Glance Projects Publications Team

Project

Estimating and Modeling Fertility Indicators

Benjamin-Samuel Schlueter, Jessica Donzowa, Emilio Zagheni, Daniela Perrotta

In many low- and middle-income countries, timely and accurate information on fertility indicators is still lacking. We fill these gaps by using aggregate data from Facebook and by collecting primary data via targeted online surveys.  Detailed Description

Prediction of mean age at childbearing for men in 2017 based on data from Facebook’s Advertising Platform

Prediction of Male mean age at childbearing in 2017 for countries without UN ground truth data. © MPIDR

Publications

Schlueter, B.; Bruno, S.; Alexander, M. J.:

A Bayesian parametric model to estimate and reconstruct male age-specific fertility rates

MPIDR Working Paper WP-2026-006. (2026)    

Schlueter, B.; Root, L.; Alexander, M. J.:

Trends and patterns in childlessness by age, race, ethnicity, and education in the United States: a research note

MPIDR Working Paper WP-2026-005. (2026)    

Donzowa, J.; Kühne, S.; Zindel, Z.:

From clicks to quality: assessing advertisement design’s impact on social media survey response quality

methods, data, analyses, 1–42. (2025)    

Donzowa, J.; Perrotta, D.; Zagheni, E.:

Assessing self-selection biases in Facebook-recruited online surveys: evidence from the COVID-19 Health Behavior Survey

PLOS One 20:7, e0326884–e0326884. (2025)    

Rampazzo, F.; Zagheni, E.; Weber, I. G.; Testa, M.R.; Billari, F. C.:

Mater certa est, pater numquam: what can Facebook advertising data tell us about male fertility rates?

In: Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018): 25-28 June 2018, Stanford, California, 672–675. Palo Alto, CA: AAAI Press. (2018)